How do graph neural networks (GNNs) leverage graph structures to learn representations of nodes and edges in graph data?

Graph neural networks (GNNs) are a class of neural network architectures designed to operate on graph-structured data such as social networks, citation networks, and molecular graphs. GNNs propagate information between nodes through graph convolution operations, allowing them to learn representations that capture the structural and relational information of the graph. GNNs have applications in graph classification, node classification, and link prediction tasks.